Here we compare mESCs from four datasets:
The Lackford data is based on E14Tg2a cells (129/Ola strain) cultured in ESGRO Complete PLUS Clonal Grade Medium (Millipore), which uses a specific (but proprietary) GSK3β inhibitor and a serum-free condition that does not require LIF.
The Nam data lists 129S5/SvEvBrd as the strain. Culture conditions are not specified in article, supplement, GEO, or SRA.
The Guo data is based on OG2 transgenic mouse embryonic stem cells, with a strain background of 1/2 129SVJ+3/8 C57B6+1/8 CBA. Cells were cultured on feeder layers with 2iL medium (high glucose DMEM, 15%FBS, NEAA, GlutaMAX, LIF) plus 2i (1 mm PD0325901, 3 mm CHIR99021) and LIF.
The Bleckwehl data is based on E14Tg2a cells (129/Ola strain) cultured in 2i+LIF conditions (serum-free N2B27 medium supplemented with MEK inhibitor PD0325901 [0.4 μM, Miltenyi Biotec], GSK3β inhibitor CHIR99021 [3 μM, Amsbio], and LIF).
Lackford used a direct RNA sequencing protocol performed by Helicos BioSciences.
Nam used a 3P-Seq protocol based on Jan et al., 2011, that uses 16-20 amplification cycles.
Bleckwehl and Guo used a 10X Chromium 3’ sequencing kit (v2 chemistry).
Lackford and Nam quantifications come from PolyASite v2 (pipeline).
Bleckwehl and Guo are quantified through the scutr-quant pipeline.
## Loading PolyASite BED files
mm10_seqinfo <- seqinfo(getBSgenome("mm10")) %>% keepStandardChromosomes
extra_cols_polyasite <- c(pct_samples="numeric", n_protocols="integer",
mean_tpm="numeric", region="factor", pas="character")
import_polyasite <- function(...) {
import(..., format="BED", extraCols=extra_cols_polyasite) %>%
mutate(tpm=score) %>%
select(name, tpm, 3:7) %>%
`seqlevelsStyle<-`("UCSC") %>%
`seqlevels<-`(value=seqlevels(mm10_seqinfo), pruning.mode='coarse') %>%
`seqinfo<-`(value=mm10_seqinfo)
}
lower_geom_point_pct <- function (data, mapping, ...) {
ggplot(data=data, mapping=mapping, ...) +
geom_point(size=0.5, alpha=0.5, pch=16, ...) +
geom_abline(linetype='dashed', color='red') +
lims(x=c(0,1), y=c(0,1))
}
lower_geom_point <- function (data, mapping, ...) {
ggplot(data=data, mapping=mapping, ...) +
geom_point(size=0.5, alpha=0.5, pch=16, ...) +
geom_abline(linetype='dashed', color='red')
}
bleckwehl_samples=c("2i_ESC"="Bleckwehl21", "d2_EB"="Bleckwehl21_EB2", "d4_EB"="Bleckwehl21_EB4")
gr_nam <- import_polyasite("data/bed/polyasite/nam14/mESC_WT.bed.gz")
gr_lackford <- import_polyasite("data/bed/polyasite/lackford14/mESC_WT.bed.gz")
sce_bleckwehl <- readRDS("data/sce/01_filtered.rada-iglesias20.utrome.txs.Rds") %>%
`[`(, .$sample %in% names(bleckwehl_samples))
sce_guo <- readRDS("data/sce/01_filtered.guo19.utrome.txs.Rds")
colData(sce_bleckwehl) %<>%
as_tibble(rownames='cell_id') %>%
mutate(sample_id=bleckwehl_samples[sample]) %>%
select(cell_id, sample_id) %>%
column_to_rownames("cell_id") %>%
DataFrame()
colData(sce_guo) %<>%
as_tibble %>%
mutate(sample_id="Guo19") %>%
select(cell_id, sample_id) %>%
column_to_rownames("cell_id") %>%
DataFrame()
sce <- cbind(sce_guo, sce_bleckwehl)
# Filter to top two non-overlapping UTRs
df_utr2 <- rowData(sce) %>%
as_tibble %>%
select(transcript_id, gene_id, gene_symbol, is_ipa, atlas.utr_type, is_blacklisted) %>%
mutate(total_umis=rowSums(counts(sce)),
tpm=total_umis*1e6/sum(total_umis, na.rm=TRUE)) %>%
#filter(!is_ipa, total_umis > 0) %>%
filter(!is_ipa, atlas.utr_type == 'multi', !is_blacklisted) %>%
group_by(gene_id) %>%
filter(dplyr::n() > 1) %>%
mutate(utr_rank=row_number(-total_umis)) %>%
ungroup() %>%
filter(utr_rank %in% 1:2)
idx_utr2 <- rowRanges(sce)[df_utr2$transcript_id,] %>%
join_overlap_self_directed() %>%
filter(transcript_id != transcript_id.overlap) %>%
{ filter(df_utr2, !(transcript_id %in% .$transcript_id)) } %>%
group_by(gene_id) %>%
filter(n() == 2) %>%
ungroup() %$%
transcript_id
df_nam <- gr_nam %>%
anchor_center() %>%
mutate(width=50) %>%
join_overlap_intersect_directed(x=rowRanges(sce)[idx_utr2,]) %>%
group_by(transcript_id) %>%
summarise(tpm=sum(tpm, na.rm=TRUE)) %>%
as_tibble() %>%
rename(Nam14=tpm)
df_lackford <- gr_lackford %>%
anchor_center() %>%
mutate(width=50) %>%
join_overlap_intersect_directed(x=rowRanges(sce)[idx_utr2,]) %>%
group_by(transcript_id) %>%
summarise(tpm=sum(tpm, na.rm=TRUE)) %>%
as_tibble() %>%
rename(Lackford14=tpm)
M_samples <- fac2sparse(sce$sample_id) %>% t
cts <- assay(sce, 'normcounts') %*% M_samples
df_sc <- (cts %*% Diagonal(ncol(cts), 1e6/colSums(cts))) %>%
`[`(idx_utr2, , drop=FALSE) %>%
as.matrix %>%
as_tibble(rownames='transcript_id')
df_tpm <- df_sc %>%
left_join(df_nam, by='transcript_id') %>%
left_join(df_lackford, by='transcript_id') %>%
mutate(across(where(is.numeric), coalesce, 0)) %>%
left_join(select(df_utr2, transcript_id, gene_id, total_umis, utr_rank),
by='transcript_id') %>%
select(transcript_id, gene_id, utr_rank, total_umis, everything())
N_SAMPLES=ncol(df_tpm) - 5
SAMPLE_COLS=c("Nam14", "Lackford14", "Guo19", "Bleckwehl21")
#SAMPLE_COLS=c("Nam14", "Lackford14", "Guo19", "Bleckwehl21", "Bleckwehl21_EB2", "Bleckwehl21_EB4")
MIN_TPM=0
df_tpm_min <- df_tpm %>%
pivot_longer(all_of(SAMPLE_COLS), names_to="sample", values_to="tpm") %>%
group_by(transcript_id) %>%
filter(all(tpm > MIN_TPM)) %>%
ungroup() %>%
pivot_wider(names_from='sample', values_from='tpm') %>%
group_by(gene_id) %>%
filter(n() == 2) %>%
ungroup()
df_tpm %>%
mutate(across(all_of(SAMPLE_COLS), . %>% { log10(1+.) })) %>%
ggpairs(columns=SAMPLE_COLS, progress=FALSE,
lower=list(continuous=wrap(lower_geom_point, alpha=0.2)),
upper=list(continuous=wrap('cor', method="spearman"))) +
## lower=list(continuous=wrap("points", alpha=0.2, size=0.2, pch=16))) +
labs(x="log10(1 + TPM)", y="log10(1 + TPM)") +
theme_bw()
ggsave("output/figure2/ed2f-mescs-all-tpm.pdf", width=6, height=6, dpi=300)
df_tpm_min %>%
mutate(across(all_of(SAMPLE_COLS), . %>% { log10(1+.) })) %>%
ggpairs(columns=SAMPLE_COLS, progress=FALSE,
lower=list(continuous=wrap(lower_geom_point, alpha=0.2)),
upper=list(continuous=wrap('cor', method="spearman"))) +
##lower=list(continuous=wrap("points", alpha=0.2, size=0.2, pch=16))) +
labs(x="log10(1 + TPM)", y="log10(1 + TPM)") +
theme_bw()
df_lui <- df_tpm %>%
pivot_longer(all_of(SAMPLE_COLS), names_to='sample', values_to="tpm") %>%
group_by(gene_id, sample) %>%
mutate(lui=tpm/sum(tpm, na.rm=TRUE),
utr_pos=row_number(as.integer(str_extract(transcript_id,
"[^.]+$")))) %>%
filter(utr_pos == max(utr_pos)) %>%
ungroup() %>%
select(gene_id, sample, lui) %>%
pivot_wider(names_from='sample', values_from='lui')
df_lui_min <- df_tpm_min %>%
pivot_longer(all_of(SAMPLE_COLS), names_to='sample', values_to="tpm") %>%
group_by(gene_id, sample) %>%
mutate(lui=tpm/sum(tpm, na.rm=TRUE),
utr_pos=row_number(as.integer(str_extract(transcript_id,
"[^.]+$")))) %>%
filter(utr_pos == max(utr_pos)) %>%
ungroup() %>%
select(gene_id, sample, lui) %>%
pivot_wider(names_from='sample', values_from='lui')
SAMPLE_COLS_LUI=seq(2, ncol(df_lui))
df_lui %>%
ggpairs(columns=SAMPLE_COLS_LUI, progress=FALSE,
lower=list(continuous=wrap(lower_geom_point_pct, alpha=0.4)),
upper=list(continuous=wrap('cor', method="spearman"))) +
##lower=list(continuous=wrap("points", alpha=0.3, size=0.1))) +
labs(x="LUI", y="LUI") +
theme_bw()
df_lui_min %>%
ggpairs(columns=SAMPLE_COLS_LUI, progress=FALSE,
lower=list(continuous=wrap(lower_geom_point_pct, alpha=0.4)),
upper=list(continuous=wrap('cor', method="spearman"))) +
##lower=list(continuous=wrap("points", alpha=0.3, size=0.1))) +
labs(x="LUI", y="LUI") +
theme_bw()
ggsave("output/figure2/ed2h-mescs-all-lui-two-utr.pdf", width=6, height=6, dpi=300)
axis_lims <- c(df_tpm$Lackford14, df_tpm$Nam14) %>%
{ log10(1 + .) } %>% { c(min(.), max(.)) }
label_rho_s <- sprintf("\"Spearman\" ~ rho == \"%0.3f\"",
cor(df_tpm$Nam14, df_tpm$Lackford14, method='spearman'))
label_lims <- axis_lims %>% { . + c(0.25*(.[2]-.[1]), 0.05*(.[1]-.[2])) }
df_tpm %>%
ggplot(aes(x=log10(Lackford14+1), y=log10(Nam14+1))) +
geom_point(size=0.3, alpha=0.4, pch=16) +
geom_abline(linetype='dashed', color='red') +
annotate("text", x=label_lims[1], y=label_lims[2], label=label_rho_s,
size=4, parse=TRUE) +
scale_x_continuous(limits=axis_lims) + scale_y_continuous(limits=axis_lims) +
labs(x="Bulk - Lackford14 [log10(TPM + 1)]", y="Bulk - Nam14 [log10(TPM + 1)]") +
theme_bw() +
theme(plot.margin=grid::unit(c(4,8,4,4), "mm"), aspect.ratio=1)
ggsave("output/figure2/fig2g-mescs-bulk-bulk-tpm.pdf", width=5, height=4)
label_rho_s <- sprintf("\"Spearman\" ~ rho == \"%0.3f\"",
cor(df_lui_min$Nam14, df_lui_min$Lackford14, method='spearman'))
df_lui_min %>%
ggplot(aes(x=Lackford14, y=Nam14)) +
geom_point(size=0.4, alpha=0.5, pch=16) +
geom_abline(linetype='dashed', color='red') +
annotate("text", x=0.25, y=0.95, label=label_rho_s, size=4, parse=TRUE) +
scale_x_continuous(limits=c(0,1), expand=c(0,0)) +
scale_y_continuous(limits=c(0,1), expand=c(0,0)) +
labs(x="Bulk - Lackford14 [LUI]", y="Bulk - Nam14 [LUI]") +
theme_bw() +
theme(plot.margin=grid::unit(c(4,8,4,4), "mm"), aspect.ratio=1)
ggsave("output/figure2/fig2j-mesc-bulk-bulk-lui-two-utr.pdf", width=5, height=4)
axis_lims <- c(df_tpm$Lackford14, df_tpm$Bleckwehl21) %>%
{ log10(1 + .) } %>% { c(min(.), max(.)) }
label_lims <- axis_lims %>% { . + c(0.25*(.[2]-.[1]), 0.05*(.[1]-.[2])) }
label_rho_s <- sprintf("\"Spearman\" ~ rho == \"%0.3f\"",
cor(df_tpm$Lackford14, df_tpm$Bleckwehl21, method='spearman'))
df_tpm %>%
ggplot(aes(x=log10(Lackford14+1), y=log10(Bleckwehl21+1))) +
geom_point(size=0.3, alpha=0.4, pch=16) +
geom_abline(linetype='dashed', color='red') +
annotate("text", x=label_lims[1], y=label_lims[2], label=label_rho_s,
size=4, parse=TRUE) +
scale_x_continuous(limits=axis_lims) + scale_y_continuous(limits=axis_lims) +
labs(x="Bulk - Lackford14 [log10(TPM + 1)]", y="scRNA-seq - Bleckwehl21 [log10(TPM + 1)]") +
theme_bw() +
theme(plot.margin=grid::unit(c(4,8,4,4), "mm"), aspect.ratio=1)
ggsave("output/figure2/fig2h-mescs-bulk-sc-tpm.pdf", width=5, height=4)
label_rho_s <- sprintf("\"Spearman\" ~ rho == \"%0.3f\"",
cor(df_lui_min$Lackford14, df_lui_min$Bleckwehl21, method='spearman'))
df_lui_min %>%
ggplot(aes(x=Lackford14, y=Bleckwehl21)) +
geom_point(size=0.4, alpha=0.5, pch=16) +
geom_abline(linetype='dashed', color='red') +
annotate("text", x=0.25, y=0.95, label=label_rho_s, size=4, parse=TRUE) +
scale_x_continuous(limits=c(0,1), expand=c(0,0)) +
scale_y_continuous(limits=c(0,1), expand=c(0,0)) +
labs(x="Bulk - Lackford14 [LUI]", y="scRNA-seq - Bleckwehl21 [LUI]") +
theme_bw() +
theme(plot.margin=grid::unit(c(4,8,4,4), "mm"), aspect.ratio=1)
ggsave("output/figure2/fig2k-mesc-bulk-sc-lui-two-utr.pdf", width=5, height=4)
axis_lims <- c(df_tpm$Guo19, df_tpm$Bleckwehl21) %>%
{ log10(1 + .) } %>% { c(min(.), max(.)) }
label_rho_s <- sprintf("\"Spearman\" ~ rho == \"%0.3f\"",
cor(df_tpm$Guo19, df_tpm$Bleckwehl21, method='spearman'))
label_lims <- axis_lims %>% { . + c(0.25*(.[2]-.[1]), 0.05*(.[1]-.[2])) }
df_tpm %>%
ggplot(aes(x=log10(Guo19+1), y=log10(Bleckwehl21+1))) +
geom_point(size=0.2, alpha=0.3, pch=16) +
geom_abline(linetype='dashed', color='red') +
annotate("text", x=label_lims[1], y=label_lims[2], label=label_rho_s, size=4, parse=TRUE) +
scale_x_continuous(limits=axis_lims) + scale_y_continuous(limits=axis_lims) +
labs(x="scRNA-seq - Guo19 [log10(TPM + 1)]", y="scRNA-seq - Bleckwehl21 [log10(TPM + 1)]") +
theme_bw() +
theme(plot.margin=grid::unit(c(4,8,4,4), "mm"), aspect.ratio=1)
ggsave("output/figure2/fig2i-mescs-sc-sc-tpm.pdf", width=5, height=4)
label_rho_s <- sprintf("\"Spearman\" ~ rho == \"%0.3f\"",
cor(df_lui_min$Guo19, df_lui_min$Bleckwehl21, method='spearman'))
df_lui_min %>%
ggplot(aes(x=Guo19, y=Bleckwehl21)) +
geom_point(size=0.4, alpha=0.5, pch=16) +
geom_abline(linetype='dashed', color='red') +
annotate("text", x=0.25, y=0.95, label=label_rho_s, size=4, parse=TRUE) +
scale_x_continuous(limits=c(0,1), expand=c(0,0)) +
scale_y_continuous(limits=c(0,1), expand=c(0,0)) +
labs(x="scRNA-seq - Guo19 [LUI]", y="scRNA-seq - Bleckwehl21 [LUI]") +
theme_bw() +
theme(plot.margin=grid::unit(c(4,8,4,4), "mm"), aspect.ratio=1)
ggsave("output/figure2/fig2l-mesc-sc-sc-lui-two-utr.pdf", width=5, height=4)
## R version 4.0.2 (2020-06-22)
## Platform: x86_64-apple-darwin13.4.0 (64-bit)
## Running under: macOS Mojave 10.14.6
##
## Matrix products: default
## BLAS/LAPACK: /Users/mfansler/miniconda3/envs/bioc_3_12/lib/libopenblasp-r0.3.10.dylib
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] parallel stats4 stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] BSgenome.Mmusculus.UCSC.mm10_1.4.0 GGally_2.1.0
## [3] cowplot_1.1.0 forcats_0.5.1
## [5] stringr_1.4.0 dplyr_1.0.5
## [7] purrr_0.3.4 readr_1.4.0
## [9] tidyr_1.1.3 tibble_3.1.0
## [11] ggplot2_3.3.3 tidyverse_1.3.0
## [13] magrittr_2.0.1 Matrix_1.3-2
## [15] BSgenome_1.58.0 Biostrings_2.58.0
## [17] XVector_0.30.0 SingleCellExperiment_1.12.0
## [19] SummarizedExperiment_1.20.0 Biobase_2.50.0
## [21] MatrixGenerics_1.2.0 matrixStats_0.58.0
## [23] plyranges_1.10.0 rtracklayer_1.50.0
## [25] GenomicRanges_1.42.0 GenomeInfoDb_1.26.4
## [27] IRanges_2.24.0 S4Vectors_0.28.0
## [29] BiocGenerics_0.36.0
##
## loaded via a namespace (and not attached):
## [1] bitops_1.0-6 fs_1.5.0 lubridate_1.7.10
## [4] RColorBrewer_1.1-2 httr_1.4.2 tools_4.0.2
## [7] backports_1.2.1 utf8_1.1.4 R6_2.5.0
## [10] DBI_1.1.1 colorspace_2.0-0 withr_2.4.1
## [13] tidyselect_1.1.0 compiler_4.0.2 cli_2.3.1
## [16] rvest_1.0.0 xml2_1.3.2 DelayedArray_0.16.0
## [19] labeling_0.4.2 scales_1.1.1 digest_0.6.27
## [22] Rsamtools_2.6.0 rmarkdown_2.7 pkgconfig_2.0.3
## [25] htmltools_0.5.1.1 highr_0.8 dbplyr_2.1.0
## [28] rlang_0.4.10 readxl_1.3.1 rstudioapi_0.13
## [31] generics_0.1.0 farver_2.1.0 jsonlite_1.7.2
## [34] BiocParallel_1.24.0 RCurl_1.98-1.2 GenomeInfoDbData_1.2.4
## [37] Rcpp_1.0.6 munsell_0.5.0 fansi_0.4.2
## [40] lifecycle_1.0.0 stringi_1.5.3 yaml_2.2.1
## [43] zlibbioc_1.36.0 plyr_1.8.6 grid_4.0.2
## [46] crayon_1.4.1 lattice_0.20-41 haven_2.3.1
## [49] hms_1.0.0 knitr_1.31 pillar_1.5.1
## [52] reprex_1.0.0 XML_3.99-0.5 glue_1.4.2
## [55] evaluate_0.14 modelr_0.1.8 vctrs_0.3.6
## [58] cellranger_1.1.0 gtable_0.3.0 reshape_0.8.8
## [61] assertthat_0.2.1 xfun_0.20 broom_0.7.5
## [64] GenomicAlignments_1.26.0 ellipsis_0.3.1
## Conda Environment YAML
name: bioc_3_12
channels:
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